intel/intel-optimized-tensorflow Docker 镜像 - 轩辕镜像

PROJECT NOT UNDER ACTIVE MANAGEMENT. This image repo will no longer be maintained by Intel.

Intel® Extension for TensorFlow*

Intel® Extension for TensorFlow* extends TensorFlow* with up-to-date feature optimizations for an extra performance boost on Intel hardware.

Intel® Extension for TensorFlow* is based on the TensorFlow PluggableDevice interface to bring Intel XPU(GPU, CPU, etc.) devices into TensorFlow* with flexibility for on-demand performance on the following Intel GPUs:

• Intel® Arc™ A-Series Graphics
• Intel® Data Center GPU Flex Series
• Intel® Data Center GPU Max Series

Note: There are two dockerhub repositories (intel/intel-extension-for-tensorflow and intel/intel-optimized-tensorflow) that are routinely updated with the latest images, however, some legacy images have not be published to both repositories.

XPU images

The images below include support for both CPU and GPU optimizations:

The following images include support for Intel® Deep Learning Essentials:

| Tag(s) | TensorFlow | ITEX | Driver | DL Essentials | Dockerfile | | ---------------------- | ----------- | -------------- | ------- | --------------- | | 2.15.0.3-xpu-pip-dl-essentials | v2.15.1 | v2.15.0.3 | 1099| 2025.0.2-6 | v0.4.0-Beta |

Run the XPU Container

bash
docker run -it --rm \
    --device /dev/dri \
    -v /dev/dri/by-path:/dev/dri/by-path \
    --ipc=host \
    intel/intel-extension-for-tensorflow:xpu

---

The images below additionally include Jupyter Notebook server:

Run the XPU Jupyter Container

bash
docker run -it --rm \
    -p 8888:8888 \
    --net=host \
    --device /dev/dri \
    -v /dev/dri/by-path:/dev/dri/by-path \
    --ipc=host \
    intel/intel-extension-for-tensorflow:2.15.0.3-xpu-pip-jupyter

After running the command above, copy the URL (something like [***]) into your browser to access the notebook server.

---

The images below are TensorFlow* Serving with GPU Optimizations:

Run the Serving GPU Container

bash
docker run -it --rm \
    -p 8500:8500 \
    --device /dev/dri \
    -v /dev/dri/by-path:/dev/dri/by-path \
    -v $PWD/workspace:/workspace \
    -w /workspace \
    -e MODEL_NAME=<your-model-name> \
    -e MODEL_DIR=<your-model-dir> \
    intel/intel-extension-for-tensorflow:serving-gpu

For more details, follow the procedure in the Intel® Extension for TensorFlow* Serving instructions.

CPU only images

The images below are built only with CPU optimizations (GPU acceleration support was deliberately excluded):

The images below additionally include Jupyter Notebook server:

Run the CPU Jupyter Container

bash
docker run -it --rm \
    -p 8888:8888 \
    --net=host \
    -v $PWD/workspace:/workspace \
    -w /workspace \
    intel/intel-extension-for-tensorflow:2.15.1-pip-jupyter

After running the command above, copy the URL (something like [***]) into your browser to access the notebook server.

---

The images below additionally include Horovod:

[!NOTE] Passwordless SSH connection is also enabled in the image, but the container does not contain any SSH ID keys. The user needs to mount those keys at /root/.ssh/id_rsa and /etc/ssh/authorized_keys.

[!TIP] Before mounting any keys, modify the permissions of those files with chmod 600 authorized_keys; chmod 600 id_rsa to grant read access for the default user account.

Setup and Run ITEX Multi-Node Container

[!IMPORTANT] Maintainence, Bug Fixes, and Releases of Intel® Extension for TensorFlow* Multi-Node Container for Xeon Processors have ceased development. The last supported version is 2.15.1. For future releases, please use the Intel® Extension for TensorFlow* Multi-Node Container for XPU.

Some additional assembly is required to utilize this container with OpenSSH. To perform any kind of DDP (Distributed Data Parallel) execution, containers are assigned the roles of launcher and worker respectively:

SSH Server (Worker)

1. Authorized Keys : /etc/ssh/authorized_keys

SSH Client (Launcher)

1. Private User Key : /root/.ssh/id_rsa

To add these files correctly please follow the steps described below.

1. Setup ID Keys

   You can use the commands provided below to generate the identity keys for OpenSSH.

   bash
   ssh-keygen -q -N "" -t rsa -b 4096 -f ./id_rsa
   touch authorized_keys
   cat id_rsa.pub >> authorized_keys
   

2. Configure the permissions and ownership for all of the files you have created so far

   bash
   chmod 600 id_rsa config authorized_keys
   chown root:root id_rsa.pub id_rsa config authorized_keys
   

3. Create a hostfile for horovod. (Optional)

   txt
   Host host1
       HostName <Hostname of host1>
       IdentitiesOnly yes
       IdentityFile ~/.root/id_rsa
       Port <SSH Port>
   Host host2
       HostName <Hostname of host2>
       IdentitiesOnly yes
       IdentityFile ~/.root/id_rsa
       Port <SSH Port>
   ...
   

4. Configure Horovod in your python script

   python
   import horovod.torch as hvd
   
   hvd.init()
   

5. Now start the workers and execute DDP on the launcher

   1. Worker run command:

      bash
      docker run -it --rm \
          --net=host \
          -v $PWD/authorized_keys:/etc/ssh/authorized_keys \
          -v $PWD/tests:/workspace/tests \
          -w /workspace \
          intel/intel-optimized-tensorflow:2.15.1-pip-multinode \
          bash -c '/usr/sbin/sshd -D'
      

   2. Launcher run command:

      bash
      docker run -it --rm \
          --net=host \
          -v $PWD/id_rsa:/root/.ssh/id_rsa \
          -v $PWD/tests:/workspace/tests \
          -v $PWD/hostfile:/root/ssh/config \
          -w /workspace \
          intel/intel-optimized-tensorflow:2.15.1-pip-multinode \
          bash -c 'horovodrun --verbose -np 2 -H host1:1,host2:1 /workspace/tests/tf_base_test.py'
      

[!NOTE] Intel® MPI can be configured based on your machine settings. If the above commands do not work for you, see the documentation for how to configure based on your network.

---

The images below are TensorFlow* Serving with CPU Optimizations:

Run the Serving CPU Container

bash
docker run -it --rm \
    -p 8500:8500 \
    --device /dev/dri \
    -v /dev/dri/by-path:/dev/dri/by-path \
    -v $PWD/workspace:/workspace \
    -w /workspace \
    -e MODEL_NAME=<your-model-name> \
    -e MODEL_DIR=<your-model-dir> \
    intel/intel-extension-for-tensorflow:serving-cpu

For more details, follow the procedure in the Intel® Extension for TensorFlow* Serving instructions.

CPU only images with Intel® Distribution for Python*

The images below are built only with CPU optimizations (GPU acceleration support was deliberately excluded) and include Intel® Distribution for Python*:

The images below additionally include Jupyter Notebook server:

The images below additionally include Horovod:

XPU images with Intel® Distribution for Python*

The images below are built only with CPU and GPU optimizations and include Intel® Distribution for Python*:

The following images include support for Intel® Deep Learning Essentials:

| Tag(s) | TensorFlow | ITEX | Driver | DL Essentials | Dockerfile | | ---------------------- | ----------- | -------------- | ------- | --------------- | | 2.15.0.3-xpu-idp-dl-essentials | v2.15.1 | v2.15.0.3 | 1099| 2025.0.2-6 | v0.4.0-Beta |

The images below additionally include Jupyter Notebook server:

[!NOTE] The support for CPU and XPU images containing Intel® Distribution for Python* are deprecated with no new releases. However, pip based images will be supported.

Build from Source

To build the images from source, clone the AI Containers repository, follow the main README.md file to setup your environment, and run the following command:

bash
cd pytorch
docker compose build tf-base
docker compose run tf-base

You can find the list of services below for each container in the group:

License

View the License for the Intel® Extension for TensorFlow*.

The images below also contain other software which may be under other licenses (such as TensorFlow*, Jupyter*, Bash, etc. from the base).

It is the image user's responsibility to ensure that any use of The images below comply with any relevant licenses for all software contained within.

* Other names and brands may be claimed as the property of others.